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<Paper uid="H01-1072">
  <Title>T&amp;quot;uSBL: A Similarity-Based Chunk Parser for Robust Syntactic Processing</Title>
  <Section position="3" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1. INTRODUCTION
</SectionTitle>
    <Paragraph position="0"> Current research on natural language parsing tends to gravitate toward one of two extremes: robust, partial parsing with the goal of broad data coverage versus more traditional parsers that aim at complete analysis for a narrowly defined set of data. Chunk parsing [1, 2] offers a particularly promising and by now widely used example of the former kind. The main insight that underlies the chunk parsing strategy is to isolate the (finite-state) analysis of nonrecursive, syntactic structure, i.e. chunks, from larger, recursive structures. This results in a highly-efficient parsing architecture that is realized as a cascade of finite-state transducers and that pur. null sues a longest-match, right-most pattern-matching strategy at each level of analysis.</Paragraph>
    <Paragraph position="1"> Despite the popularity of the chunk parsing approach, there seem to be two apparent gaps in current research:  1. Chunk parsing research has focused on the recognition of  partial constituent structures at the level of individual chunks. By comparison, little or no attention has been paid to the question of how such partial analyses can be combined into larger structures for complete utterances.</Paragraph>
    <Paragraph position="2"> 2. Relatively little has been reported on quantitative evaluations of chunk parsers that measure the correctness of the output structures obtained by a chunk parser.</Paragraph>
    <Paragraph position="3"> The main goal of the present paper is help close those two research gaps.</Paragraph>
  </Section>
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